NDLI: Integration of sensory feedback into CPG model for locomotion control of caterpillar-like robot

Content Provider	IEEE Xplore Digital Library
Author	Li, Guoyuan Li, Wei Zhang, Houxiang Zhang, Jianwei
Copyright Year	2015
Description	Author affiliation: Department of Computer Science, University of Hamburg, Vogt-Koelln-Strasse 30, 22527, Hamburg, Germany (Zhang, Jianwei) \|\| Faculty of Maritime Technology and Operations, Aalesund University College, Postboks 1517, N-6025, Aalesund, Norway (Li, Guoyuan; Li, Wei; Zhang, Houxiang)
Abstract	This paper presents an approach of sensory feedback integration into a CPG model under a hierarchical control architecture for adaptive caterpillar-like locomotion. Motivated by the simplicity of CPG models in gait generation, a sinusoidal generator is employed as the low level controller of a caterpillarlike robot. To regulate the behavior of the robot with respect to the changes of environmental conditions, a behavior adaptor is designed to integrate sensory feedback into the sine-based model, so that sensory input can be transferred into the model and further affect the output of the model to some extent. Meanwhile, a policy gradient based reinforcement learning method is adopted at the high level, aiming to learn the mapping between sensory input and reasonable responses of the robot. The optimized policy could be obtained after episodic learning. Simulation results show the caterpillar-like robot can climb over uneven terrains with the help of the sinusoidal generator and the learned policy, which verifies the effectiveness of the proposed approach.
Starting Page	303
Ending Page	308
File Size	512821
Page Count	6
File Format	PDF
ISBN	9781479978007
DOI	10.1109/ICIT.2015.7125115
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-03-17
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robot sensing systems Adaptation models Generators Robot kinematics Learning systems Biological system modeling
Content Type	Text
Resource Type	Article

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